Evidence map›Paper›PMID 41310039›Full record

ArticleNPJ digital medicine2025

A cluster randomised trial of digital messaging nudges to improve influenza vaccination uptake in China.

Qian Wang, Qian Hou, Linling Ding, Wenwen Gu, Yang Zhou, Yan Liu, Hui Xu, Hanqing He, Yachao Jia, Lance Rodewald and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Qian WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Qian HouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Linling DingZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Wenwen GuHangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), Hangzhou, China.
Yang ZhouZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Yan LiuHangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), Hangzhou, China.
Hui XuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Hanqing HeZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Yachao JiaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Lance RodewaldNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Wenzhou YuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Zhijie AnNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.
Lixin HaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China. haolx@chinacdc.cn.
Zhiyuan HouSchool of Public Health, Fudan University, Shanghai, China. zyhou@fudan.edu.cn.
Zundong YinNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China. yinzd@chinacdc.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As an effective strategy for preventing influenza, vaccination uptake remains suboptimal. This cluster randomised trial evaluated digital messaging nudges on influenza vaccination among children aged 6-35 months and their parents in China. During 2024-25 flu season, 36 community health centres were randomly assigned to one of six arms by message framing (gain vs. loss) and evidence type (statistical vs. narrative), and standard message and no-message control, with 2,011 parents of age-eligible children enroled. For childhood vaccination, loss-framed narrative arm achieved the highest uptake (24.0% vs. control 9.4%; adjusted RR 2.50, 95% CI 1.27-4.94), and effects sustained at five-month follow-up. Loss-framed statistical arm (16.9% vaccination uptake) was significantly effective only at one-month follow-up (RR 2.73; 95% CI 1.50-4.99), with effects attenuating rapidly. Both standard and gain-framed messages had no significant effect on childhood vaccination. Loss-framed narrative digital nudges offer a scalable strategy to improve childhood vaccination. Clinical trial registration: ChiCTR2400092213.

Identifiers

PMID41310039
PMCPMC12660379

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.